(1) Field of Invention
The present invention relates to a system for flexible cognitive perception and action selection and, more particularly, to a system for flexible cognitive perception and action selection which uses distributed representations of multiple hypotheses to interpret perceptual information in an unfamiliar environment.
(2) Description of Related Art
A machine is considered intelligent if it can perceive its environment and take actions that maximize its chance of success at achieving goals. Several systems exist that perceive an environment and assess a situation. For instance, intelligence systems and security systems report an estimate of a situation if it meets or exceeds certain criteria. Additionally, robotic systems use estimates to select actions or plans. However, it is very difficult to program such systems to recognize and understand a non-stationary environment. Fuzzy networks can make a fuzzy match to the closest known situation or situations, however, inefficiencies still exist.
In “Copycat: A Computer Model of High-Level Perception and Conceptual Slippage in Analogy-Making”, Computer Science, 1990, Mitchell builds conceptual (semantic) hierarchies on input data in a process loosely related to perception in the visual cortex. Competing hypotheses are built in parallel, and a form of simulated annealing is used to eliminate the weaker ones. There is no learning involved; rather, a population of elements of an interpretation is designed a priori, along with a set of hypotheses. The simulated annealing technique is critical for deciding when a solution has been reached and for deciding which hypotheses survive, but it depends heavily on a “temperature” control mechanism, which has only been designed for a very limited domain. The system does not interact with the environment to evaluate potential solutions.
In “Distributed Representations of Structure: A Theory of Analogical Access and Mapping”, Psychological Review, 104: 427-466, 1997 and in “A Symbolic Connectionist Theory of Relational Interference and Generalization”, Psychological Review, 110: 220-263, 2003, Hummel and Holyoak describe a network pattern-matcher, which matches inputs to hand-built ontological descriptions. Ontology building is difficult, time-consuming, and hard to automate. In their papers, Hummel and Holyoak present a signaling system to find and retrieve relevant stored patterns based on temporal signals describing features of the input data, which would be impractical for sufficiently complex inputs. Furthermore, there is no way to compare different patterns in parallel or compute them directly. Instead, they must be done sequentially and some metric must be stored for comparison.
Additionally, Gentner finds similarities between semantic hierarchies by structural similarity using an analogical reasoner in “Structure-Mapping: A Theoretical Framework for Analogy”, Cognitive Science, 7: 155-170, 1983. The reasoner is a pattern matcher, and cannot learn new structures.
Finally, U.S. Pat. No. 5,659,666, entitled, “Device for the Autonomous Generation of Useful Information” issued to Thaler discloses a neural network device for simulating human creativity. An Imagination Engine (IE) net is trained to produce input/output (I/O) maps in some predetermined knowledge domain, and there is a way to perturb the IE to change the I/O mapping. However, Thaler does not disclose a mechanism for ensuring that the new I/O mapping is internally consistent, other than testing the randomly generated mappings. His approach does not use or take into account the hierarchical structure of knowledge.
Thus, a continuing need exists for a system that can learn features of an unfamiliar environment, adapt readily to non-stationary environments, and adjust to different domains without much reprogramming, all while taking into account the hierarchical structure of knowledge.